title: Rough sets for in silico identification of differentially expressed miRNAs.
abstract: The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine.
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.
authors.forename: Sushmita
authors.initials: S
authors.lastname: Paul
authors.forename: Pradipta
authors.initials: P
authors.lastname: Maji
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D035683:MicroRNAs / Q000032:analysis / Q000235:genetics* / Q000378:metabolism; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D035683:MicroRNAs
keywords: bootstrap error; feature selection; microRNA; support vector machine